Heart failure & cardiomyopathies
Risk prediction models for incident heart failure: a systematic review and meta-analysis
Authors' conclusion
The PREVENT, ARIC and MESA risk scores demonstrate promising performance and should be prioritised for further validation and progression to impact studies.
Study context
- Population
- Derivation or validation studies for incident heart failure (HF) prediction modelsstudies representing 238 models, from 24,531 publications identified
- Participants
- 76 studies
- Outcome
- Discrimination among models validated in at least one cohort
Methodological detailsrandom-effects meta-analyses
- Random-effects meta-analyses
Pooled result
As reported in the abstract
- Models validated in at least one cohort
- 64
- Models with moderate discrimination
- four
- Models with high discrimination
- eight
Further pooled results
Calibration
- In patients with low predicted risk, calibration may have been adequate
Most promising models
- PREVENT, Atherosclerosis Risk in Communities (ARIC) and Multi-Ethnic Study of Atherosclerosis (MESA) models most promising for further validation and impact studies
Risk of bias
- Risk of bias high in assessments
- 82.9%
Characteristics of externally validated models
- Derived using machine learning
- 14
- Incorporated novel biomarkers such as proteomics and polygenic risk scores
- six
- Included measures of social determinants of health as predictors
- nine
Original publication
Risk prediction models for incident heart failure: a systematic review and meta-analysis. Heart. Published 20 April 2026. doi:10.
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